code: def forward(self, inputs, mask=None):
      # inputs [b, s, h]
      attn_output = self.self_attention(inputs)  # [b, s, h]
      attn_output = self.dropout1(attn_output)  # [b, s, h]
      ln1_output = self.layernorm1(inputs + attn_output)  # [b, s, h]
      fc_output = self.fully_connected(ln1_output)  # [b, s, h]
      fc_output = self.dropout2(fc_output)  # [b, s, h]
      return self.layernorm2(ln1_output + fc_output)
  
imports: import logging
  import torch
  from torch import nn
  from torch.nn import functional
  from ludwig.utils.torch_utils import get_activation, LudwigModule
label_desc: Use image node_id 3 for calling the TransformerBlock obj's underlying member method code with example usage: obj.forward(inputs, mask) and returns: self